In recent years, various vehicle yaw stability control systems that prevent vehicles from spinning out and drifting using differential braking have been developed. Lateral velocity (or side slip angle) is one of the most important vehicle dynamic variables for these systems and is also crucial for many other chassis control functions. In critical dynamic situations, lateral velocity is necessary to detect and then control an unstable vehicle which may have normal yaw rates. Also in these situations, the longitudinal vehicle velocity cannot be accurately measured by wheel speed because of excessive wheel slip. Hence a successful vehicle dynamics control must involve an accurate determination of the vehicle lateral and longitudinal velocities. Although it is possible to measure vehicle velocities directly by using dedicated measuring devices such as optical sensor and GPS, there are practical issues such as cost, accuracy and reliability that prevent the use of such devices on production vehicles.
The vehicle state estimation algorithms implemented on a production vehicle for vehicle dynamic control purposes are normally based on dead reckoning sensors only, such as wheel/steering encoders and inertia sensors which are utilized to predict the high frequency behavior of the vehicle. The vehicle state estimates may be obtained from a physical vehicle model, or via integration of the inertial sensor signals, or a combination of both. The estimation accuracy, however, can be very crude for a lot of maneuvers/road conditions, which in turn severely limits the control performance. One reason is that the vehicle model is only effective in the linear region. Another, perhaps more important, reason is that there is simply not enough inertia information. In order to accurately estimate vehicle states in all operating modes, a full six-degree-of-freedom inertial measurement unit (IMU) may be used. A typical IMU consists of three accelerometers and three gyroscopes mounted in a set of three orthogonal axes. The IMU measures the acceleration and the rotation rate of the vehicle in all three dimensions at a high sampling rate, typically at frequencies higher than 100 Hz. From this information, the velocity of the vehicle may be derived via mathematical integration. Vehicle position and heading are generally not observable without external information.
Recent progress in the development of Micro-Electro Mechanical Systems (MEMS) has made it possible to put IMU on production vehicles because of their small size, low cost and ruggedness. The reduction in size and cost, especially cost, however, has also led to a drop in accuracy of the inertial unit as a whole. The predominant error sources in the inertial sensors, whether they are gyros or accelerometers, are bias, scale factors and random walk. These errors are added up via mathematical integration, and may lead to large drifts in the attitude and velocity estimates, unless external absolute sensors are used to constantly bound the errors.
In practice, all inertia sensing systems are aided in some way by low frequency external sensors, such as global positioning system (GPS), Doppler radar, or star trackers to name a few. Due to the increasing popularity and decreasing cost of GPS, a lot of effort has been devoted to the development of GPS aided inertial systems for vehicle control purpose. While fairly good estimation accuracy may be attained in open sky environment using this approach, the performance deteriorates when the satellite signals bounce off of reflective surfaces such as tall buildings and other structures in the “urban canyon.” In the worst case, when fewer than three or four satellites can be “seen” (i.e., driving through a tunnel), the GPS provides no information to bound the errors associated with high frequency inertia sensors. Another disadvantage is that GPS devices are not at all common and/or cost effective on current production vehicles.
Therefore, there is a significant need for a low-cost device that provides accurate and robust estimate of the vehicle lateral velocity and longitudinal velocity.